Academic overview
My academic path is built on two complementary degrees: a rigorous foundation in applied mathematics, followed by a specialization in computer science and data science.
Bachelor’s Degree in Applied Mathematics
2019 — 2023
Initial academic training in analysis, algebra, probability, statistics, differential equations, modeling, and scientific computing.
Bachelor’s in Computer Science — Data Science
Final term in progress • seeking Summer 2026 internship
Further development in software engineering, data science, databases, systems, networks, architecture, and applied projects.
Path progression
Applied Mathematics → Computer Science → Data / Software / Platforms
This progression makes my profile especially coherent: a strong mathematical foundation for modeling and analysis, extended by advanced training in computing focused on systems, data, and real-world projects.
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All courses
Fall 2023
Web Programming
Introductory web development course focused on core web concepts, website creation, and the main front-end and back-end technologies. The course helped me understand how the web works, the client-server model, HTML structuring, CSS styling, interactivity with JavaScript, and server-side basics with PHP.
Introduction to Object-Oriented Programming
Introductory course to programming and object-oriented design. The course focused on designing algorithms, implementing them in Java, and structuring applications using fundamental object-oriented programming principles.
Computer Architecture
Course introducing the internal architecture of modern computers and the interactions between hardware and software. It explores the fundamental components of computer systems, the representation of digital information, and the architectural principles that influence software performance.
Probability and Statistics
Introductory course in probability and applied statistics focused on probabilistic models, descriptive statistics, and the foundations of statistical inference. The course provided a strong basis for data analysis, sampling, and the interpretation of statistical results.
Winter 2024
Data Structures and Algorithms
Core course on organizing, manipulating, and searching data using classical data structures and their associated algorithms. The course helped me learn how to choose the right structure for a given problem, compare algorithmic approaches, and analyze their efficiency in terms of time and memory.
Introduction to User Interfaces
Introductory course on the design and evaluation of user interfaces. The course helped me understand the foundations of software ergonomics, user-centered design, and graphical interface programming with JavaFX, while applying principles related to quality, accessibility, and interface organization.
Mathematics for Computer Scientists I
Course in discrete mathematics and logic applied to computer science. It provided the foundational mathematical tools needed to model computing problems, reason rigorously, and understand core concepts such as logic, sets, graphs, and Boolean algebra.
Databases I
Introductory course on relational databases covering both theoretical foundations and practical aspects. It helped me learn how to model data, design relational schemas, use SQL to manipulate data, and understand normalization and integrity principles in a database management system.
Fall 2024
Analysis and Modeling
Course focused on the early phases of software development, especially requirements analysis, object-oriented modeling, and software specifications. It helped me understand how to transform business needs into structured models that support software design and implementation.
Advanced Object-Oriented Concepts
Advanced object-oriented programming course focused on higher-level software design and development mechanisms. It allowed me to go beyond core OOP concepts by studying generics, design patterns, application robustness, event-driven programming, and advanced concepts such as design by contract and aspect-oriented programming.
Mathematics for Computer Scientists II
Applied mathematics course for computer science covering advanced topics useful in several domains, including formal languages, automata, matrix algebra, numerical analysis, cryptography, and data compression. It helped me connect mathematical concepts to concrete computing implementation problems.
Introduction to Data Science
Introductory data science course focused on the use of modern languages and libraries to manipulate, analyze, and visualize data. It helped me build practical foundations in Python and R, work with data structures, use specialized libraries, and approach preprocessing and analysis tasks such as linear regression.
Operating Systems
Introductory course on the fundamental concepts of operating systems, with a strong focus on systems programming and experimentation under open environments such as UNIX and Linux. It helped me understand how an operating system manages processes, threads, memory, files, I/O, and network communication, while developing concrete system utilities.
Winter 2025
Software Design
Course focused on the design of high-quality software, as a continuation of analysis and modeling. It helped me deepen my understanding of the design process, use modern techniques such as UML, design patterns, and software architecture styles, and complete a team project covering multiple stages of software development.
Algorithm Analysis and Design
Course focused on the theoretical analysis and design of efficient algorithms. It helped me deepen my understanding of complexity, compare several algorithmic strategies, and choose the most appropriate methods depending on the problem, while considering correctness, efficiency, and application context.
Computer Networks I
Introductory course on computer network architectures, focused on the mechanisms that enable data exchange between computers. The course helped me understand layered architectures, communication protocols, local area networks, virtual circuits, and the fundamentals of IP networking.
Modeling and Simulation
Course focused on theoretical and practical techniques for modeling, simulation, and data analysis. It allowed me to work across several stages of a data science pipeline: data collection, cleaning, preparation, visualization, dimensionality reduction, modeling, and the application of machine learning algorithms on real datasets.
Databases II
Advanced database course focused on the technical and internal aspects of database management systems. It helped me better understand storage, indexing, query processing, transaction management, concurrency, security, and the links between databases, web applications, and data analysis.
Fall 2025
Computer Networks II
Advanced computer networks course focused on Internet technologies, client/server architectures, network administration, and security. It helped me study the TCP/IP model, routing, sockets, VLANs, as well as information security principles and enterprise network design.
Legal Aspects of Computing
Introductory course on the legal dimensions of computing, software, and the Internet. It helped me understand the main laws and issues related to intellectual property, contracts, personal data protection, software licensing, domain names, and electronic commerce.
Data Warehouse Management and Mobile Programming
Course focused on non-relational databases, large-scale data modeling, and their use in web or mobile applications. It helped me understand the differences between relational and NoSQL databases, use MongoDB, model non-normalized schemas, and manipulate large-scale data from a data-oriented perspective.
Science, Technology and Society
Critical thinking course on the relationships between science, technology, and society. It helped me examine the social, ethical, and philosophical impacts of scientific and technological innovation, analyze issues such as social responsibility and the viability of new technologies, and develop structured thinking on contemporary dilemmas related to STEM and artificial intelligence.
Software Engineering
Course focused on the fundamental principles of software engineering and the production of quality software. It helped me deepen my understanding of reusable and maintainable design, quality assurance, testing, software project management, and software evolution, while applying design patterns and design principles in a session project.
Winter 2026
Machine Learning and Applications
Advanced introductory course in machine learning covering both causal and non-causal approaches as well as the foundations of deep learning. The course aims to apply machine learning algorithms to different types of data, understand model training and validation issues, and explore modern data-driven methods.
Electronic Commerce
Introductory electronic commerce course focused on core digital business concepts and their practical application through the development of a transactional website project. The course aims to integrate several technical and functional skills to design an effective e-commerce solution, including data modeling, interface design, online payment, marketing, and project management.
Introduction to Mobile Application Development (Android)
Introductory course on Android mobile application development focused on best practices, application lifecycle, mobile interfaces, and client-server communication. The course aims to help me design functional, structured, and collaborative Android applications using Java, Android Studio, and Git.
Academic foundation
Applied mathematics
A rigorous initial training in analysis, algebra, probability, statistics, differential equations, geometry, and scientific computing.
Path evolution
Transition into computer science
This mathematical foundation naturally supported my development in algorithms, programming, data science, systems, databases, and software design.
Current positioning
Hybrid mathematics + computing profile
My path combines abstract reasoning, quantitative modeling, and concrete execution across software, data, and digital platform projects.
Previous degree and academic equivalency
Before starting my program at UQTR, I completed a full degree in applied mathematics at the University of Dschang. This training gave me a strong theoretical foundation in modeling, scientific computing, analysis, probability, statistics, and abstract reasoning.
Bachelor’s Degree in Applied Mathematics
Complete training in applied mathematics covering algebra, analysis, probability, statistics, differential equations, numerical analysis, topology, geometry, computing, and several scientific applications. This program gave me a rigorous theoretical foundation, strong abstraction skills, and solid abilities in mathematical modeling, scientific computing, and quantitative analysis.
WES academic equivalency
WES evaluation: Canadian equivalency recognized as a Bachelor’s degree (four years).
Year 1
LMD Semester 1
Courses taken
- MAT111Algebra I: Fundamental Algebraic Concepts
- MAT121Analysis I: Analysis of the Real Vector Line
- MAT131Introduction to Computer Science
- MAT141Vector Analysis
- MAT151aEnglish Language I
- MAT161Mechanics I
Advanced concepts covered
- Foundations of algebra and mathematical reasoning: sets, relations, mappings, operations, and first algebraic structures.
- Study of real-valued functions of one real variable: limits, continuity, variations, and analytical reading of function behavior.
- First tools in geometry and vector analysis: vectors, vector operations, and geometric interpretation in the plane and space.
- Introduction to computing concepts: information representation, processing logic, and fundamentals of algorithms and programming.
- Mathematical applications to classical mechanics: kinematics, dynamics, and modeling of simple physical phenomena.
- Development of scientific English vocabulary for academic reading and communication.
LMD Semester 2
Courses taken
- MAT112Algebra II: Linear Algebra
- MAT122Analysis II: Differential Calculus
- MAT132Integral Calculus and Ordinary Differential Equations
- MAT142Statistics I
- MAT152Introduction to Algorithms and Programming
- MAT162Electrostatics
Advanced concepts covered
- Linear algebra: matrices, determinants, linear systems, vector spaces, bases, dimension, and linear mappings.
- Differential calculus: differentiation, fundamental theorems, local and global study of functions, and elementary optimization.
- Integral calculus: antiderivatives, definite integrals, integration techniques, and geometric and physical interpretations.
- Ordinary differential equations: solving first-order equations and selected simple linear models.
- Descriptive statistics: organizing, summarizing, and interpreting quantitative data using core indicators.
- Algorithms and programming: variables, control structures, arrays, functions, logical problem decomposition, and writing simple programs.
- Physical applications through electrostatics and mathematical modeling of elementary electrical phenomena.
Year 2
LMD Semester 3
Courses taken
- MAT211Linear Algebra II
- MAT221Analysis III: Metric Spaces and Series
- MAT231Differential Calculus on ℝn
- MAT241Probability Calculation
- MAT261Theory of Behavior
- MAT251aEnglish Language II
Advanced concepts covered
- Advanced linear algebra: reduction of endomorphisms, eigenvalues, eigenvectors, and the structure of linear transformations.
- Metric spaces and convergence: distance, neighborhoods, sequences, completeness, and the first abstract frameworks of modern analysis.
- Numerical series and asymptotic behavior useful in analysis and approximation.
- Differential calculus on ℝn: multivariable functions, partial derivatives, gradient, differential, and extrema.
- Probability: probability spaces, random variables, common distributions, expectation, variance, and independence.
- Analytical or modeling-oriented study of behavior depending on the local program content.
- Strengthening scientific and academic English.
LMD Semester 4
Courses taken
- MAT212Linear Algebra III
- MAT222Analysis IV: Integral Calculus on ℝn
- MAT232Scientific Computation
- MAT242Computer Architectures
- MAT252Statistics II
- MAT272Market Theory
Advanced concepts covered
- Advanced linear algebra: deeper vector structures, reduction, matrix interpretation, and abstract computational tools.
- Integral calculus on ℝn: multiple integrals, integration domains, change of variables, and geometric interpretations.
- Scientific computing and numerical methods: approximation, numerical equation solving, stability, and error.
- Computer architecture: information representation, memory, processor, hardware organization, and machine logic.
- Statistics II: deeper inference, estimation, and quantitative data analysis.
- Market theory: quantitative reading and modeling of economic or decision mechanisms depending on the track followed.
Year 3
LMD Semester 5
Courses taken
- MAT311General Topology
- MAT321Groups and Rings
- MAT331Affine and Projective Geometry
- MAT341Measure and Integration
- MAT351Differential Equations
- MAT361aEnglish Language III
Advanced concepts covered
- General topology: open and closed sets, continuity, compactness, connectedness, and the abstract language of modern analysis.
- Abstract algebra: groups, rings, morphisms, and the core structures of modern algebra.
- Affine and projective geometry: transformations, invariants, and structured geometric modeling.
- Measure and integration: measurable functions, the framework of modern integration, and advanced tools useful in analysis and probability.
- Advanced differential equations: systems, qualitative behavior, and continuous modeling.
- Advanced scientific English for reading and communicating academic content.
LMD Semester 6
Courses taken
- MAT312Differential Calculus
- MAT322Complex Variables
- MAT332Introduction to Differential Geometry
- MAT342Numerical Analysis
- MAT352Set Theory
- MAT362Financial Mathematics
Advanced concepts covered
- Advanced differential calculus and deeper analytical tools useful for modeling and optimization.
- Complex variables: complex numbers, holomorphic functions, and the foundations of complex analysis.
- Differential geometry: local study of curves, surfaces, and differentiable objects.
- Numerical analysis: discretization, approximation, numerical solving, convergence, and stability of methods.
- Set theory: formal foundations of mathematics, relations, functions, and set-based structures.
- Financial mathematics: discounting, interest, annuities, valuation, and quantitative financial modeling.
Professional experience and field work
Alongside my academic journey, I have held practical roles in professional environments, both in IT support and data analysis.
Data Analyst Intern
Data analysis internship focused on business data exploration, statistical analysis, and the creation of interactive dashboards to support data-driven decision making.
Main responsibilities
- Analyzed sales data and trends using Python (pandas, matplotlib).
- Optimized inventory management based on data analysis.
- Developed interactive dashboards and reports using Tableau.
- Built statistical sales forecasts using regression models.
- Documented analyses and communicated insights to stakeholders.
Technologies / environment
IT Technician
Responsible for technical support, maintenance of IT equipment, and assisting users in a professional computing environment.
Main responsibilities
- Installed, configured, and maintained computer hardware and software.
- Provided technical support, troubleshooting, and incident resolution.
- Trained and assisted users with IT tools and systems.
- Managed IT environments and provided operational support.
Technologies / environment